{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a6e7d45-1591-49be-b98f-5cb9a54f3e31",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 【开源实习】针对任务类型Image-to-Text，开发可在香橙派AIpro开发板运行的应用\n",
    "任务编号：#ICJ6EP  \n",
    "任务链接：[【开源实习】针对任务类型Image-to-Text，开发可在香橙派AIpro开发板运行的应用](https://gitee.com/mindspore/community/issues/ICJ6EP)  \n",
    "\n",
    "\n",
    "## 环境准备\n",
    "开发者拿到香橙派开发板后，首先需要进行硬件资源确认，镜像烧录及CANN和MindSpore版本的升级，才可运行该案例，具体如下：\n",
    "\n",
    "开发板：香橙派Aipro或其他同硬件开发板  \n",
    "开发板镜像: Ubuntu镜像  \n",
    "CANN Toolkit/Kernels：8.0.0.beta1  \n",
    "MindSpore: 2.6.0  \n",
    "MindSpore NLP: 0.4.1  \n",
    "Python: 3.9\n",
    "\n",
    "### 镜像烧录\n",
    "运行该案例需要烧录香橙派官网ubuntu镜像，烧录流程参考[昇思MindSpore官网--香橙派开发专区--环境搭建指南--镜像烧录](https://www.mindspore.cn/tutorials/zh-CN/r2.7.0rc1/orange_pi/environment_setup.html) 章节。\n",
    "\n",
    "### CANN升级\n",
    "CANN升级参考[昇思MindSpore官网--香橙派开发专区--环境搭建指南--CANN升级](https://www.mindspore.cn/tutorials/zh-CN/r2.7.0rc1/orange_pi/environment_setup.html)章节。\n",
    "\n",
    "### MindSpore升级\n",
    "MindSpore升级参考[昇思MindSpore官网--香橙派开发专区--环境搭建指南--MindSpore升级](https://www.mindspore.cn/tutorials/zh-CN/r2.7.0rc1/orange_pi/environment_setup.html)章节。\n",
    "\n",
    "### 权重加载\n",
    "此处使用MindSpore NLP套件加载`Salesforce/blip-image-captioning-base`模型权重，该套件包含了许多自然语言处理的常用方法，可以方便地加载和使用modelers的模型权重。  \n",
    "BLIP是由Salesforce研究团队于2022年提出的多模态预训练框架，旨在突破视觉与语言的跨模态理解瓶颈，实现更高效、更通用的图像与文本交互能力。\n",
    "在本次任务中，我们将基于昇思MindSpore在香橙派开发板上运行Blip-base模型，体验图像生成文本的功能。  \n",
    "注意：首次运行时请耐心等待模型下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed8f6416",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: pip in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (25.2)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: mindnlp==0.4.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (0.4.1)\n",
      "Requirement already satisfied: mindspore>=2.2.14 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.6.0)\n",
      "Requirement already satisfied: tqdm in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.67.1)\n",
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      "Requirement already satisfied: datasets in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.0.0)\n",
      "Requirement already satisfied: evaluate in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.5)\n",
      "Requirement already satisfied: tokenizers==0.19.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.19.1)\n",
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      "Requirement already satisfied: pytest==7.2.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (7.2.0)\n",
      "Requirement already satisfied: pillow>=10.0.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindnlp==0.4.1) (11.3.0)\n",
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      "Requirement already satisfied: iniconfig in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.1.0)\n",
      "Requirement already satisfied: packaging in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (25.0)\n",
      "Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.6.0)\n",
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      "Requirement already satisfied: tomli>=1.0.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.2.1)\n",
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      "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (2025.3.0)\n",
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      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (1.1.5)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.26.4)\n",
      "Requirement already satisfied: protobuf>=3.13.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (6.31.1)\n",
      "Requirement already satisfied: asttokens>=2.0.4 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (3.0.0)\n",
      "Requirement already satisfied: scipy>=1.5.4 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.13.1)\n",
      "Requirement already satisfied: psutil>=5.6.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (5.9.0)\n",
      "Requirement already satisfied: astunparse>=1.6.3 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.6.3)\n",
      "Requirement already satisfied: dill>=0.3.7 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (0.3.8)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (0.45.1)\n",
      "Requirement already satisfied: six<2.0,>=1.6.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (1.17.0)\n",
      "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (21.0.0)\n",
      "Requirement already satisfied: pandas in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (2.3.1)\n",
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      "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.70.16)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (3.12.14)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (2.6.1)\n",
      "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (1.4.0)\n",
      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (5.0.1)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (1.7.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (6.6.3)\n",
      "Requirement already satisfied: propcache>=0.2.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (0.3.2)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (1.20.1)\n",
      "Requirement already satisfied: idna>=2.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.1) (3.10)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (3.4.2)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.5.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2025.7.14)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2025.2)\n",
      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (2.5.0)\n",
      "Requirement already satisfied: hypothesis<7,>=6.14 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (6.136.2)\n",
      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.1) (2.4.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: opencv-python in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (4.12.0.88)\n",
      "Collecting numpy<2.3.0,>=2 (from opencv-python)\n",
      "  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/15/31/9dffc70da6b9bbf7968f6551967fc21156207366272c2a40b4ed6008dc9b/numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.9 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.26.4\n",
      "    Uninstalling numpy-1.26.4:\n",
      "      Successfully uninstalled numpy-1.26.4\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "mindspore 2.6.0 requires numpy<2.0.0,>=1.20.0, but you have numpy 2.0.2 which is incompatible.\n",
      "pyctcdecode 0.5.0 requires numpy<2.0.0,>=1.15.0, but you have numpy 2.0.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-2.0.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: pillow in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (11.3.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting numpy==1.26.4\n",
      "  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6d/64/c3bcdf822269421d85fe0d64ba972003f9bb4aa9a419da64b86856c9961f/numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.2 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 2.0.2\n",
      "    Uninstalling numpy-2.0.2:\n",
      "      Successfully uninstalled numpy-2.0.2\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-1.26.4\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: sympy in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (1.14.0)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/miniconda3/envs/img/lib/python3.9/site-packages (from sympy) (1.3.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip\n",
    "!pip install mindnlp==0.4.1\n",
    "!pip install opencv-python\n",
    "!pip install pillow\n",
    "!pip install numpy==1.26.4\n",
    "!pip install sympy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ecddeec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/miniconda3/envs/img/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/usr/local/miniconda3/envs/img/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/usr/local/miniconda3/envs/img/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/usr/local/miniconda3/envs/img/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import cv2  \n",
    "import mindspore\n",
    "import numpy as np\n",
    "from IPython.display import clear_output, display\n",
    "from mindnlp.transformers import BlipForConditionalGeneration, BlipProcessor\n",
    "from mindspore import Tensor, context\n",
    "from PIL import Image\n",
    "\n",
    "# 导入镜像源\n",
    "os.environ[\"HF_ENDPOINT\"] = \"https://hf-mirror.com\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d70ab4-3288-4634-b64e-905b3bd25dca",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cced0607",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/miniconda3/envs/img/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
      "  warnings.warn(\n",
      "BlipTextLMHeadModel has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BlipForConditionalGeneration(\n",
       "  (vision_model): BlipVisionModel(\n",
       "    (embeddings): BlipVisionEmbeddings(\n",
       "      (patch_embedding): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "    )\n",
       "    (encoder): BlipEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0-11): 12 x BlipEncoderLayer(\n",
       "          (self_attn): BlipAttention(\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "            (qkv): Linear (768 -> 2304)\n",
       "            (projection): Linear (768 -> 768)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): BlipMLP(\n",
       "            (activation_fn): GELU(approximate='none')\n",
       "            (fc1): Linear (768 -> 3072)\n",
       "            (fc2): Linear (3072 -> 768)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (text_decoder): BlipTextLMHeadModel(\n",
       "    (bert): BlipTextModel(\n",
       "      (embeddings): BlipTextEmbeddings(\n",
       "        (word_embeddings): Embedding(30524, 768, padding_idx=0)\n",
       "        (position_embeddings): Embedding(512, 768)\n",
       "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (encoder): BlipTextEncoder(\n",
       "        (layer): ModuleList(\n",
       "          (0-11): 12 x BlipTextLayer(\n",
       "            (attention): BlipTextAttention(\n",
       "              (self): BlipTextSelfAttention(\n",
       "                (query): Linear (768 -> 768)\n",
       "                (key): Linear (768 -> 768)\n",
       "                (value): Linear (768 -> 768)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (output): BlipTextSelfOutput(\n",
       "                (dense): Linear (768 -> 768)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (crossattention): BlipTextAttention(\n",
       "              (self): BlipTextSelfAttention(\n",
       "                (query): Linear (768 -> 768)\n",
       "                (key): Linear (768 -> 768)\n",
       "                (value): Linear (768 -> 768)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (output): BlipTextSelfOutput(\n",
       "                (dense): Linear (768 -> 768)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): BlipTextIntermediate(\n",
       "              (dense): Linear (768 -> 3072)\n",
       "              (intermediate_act_fn): GELU(approximate='none')\n",
       "            )\n",
       "            (output): BlipTextOutput(\n",
       "              (dense): Linear (3072 -> 768)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (cls): BlipTextOnlyMLMHead(\n",
       "      (predictions): BlipTextLMPredictionHead(\n",
       "        (transform): BlipTextPredictionHeadTransform(\n",
       "          (dense): Linear (768 -> 768)\n",
       "          (transform_act_fn): GELU(approximate='none')\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (decoder): Linear (768 -> 30524)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "processor = BlipProcessor.from_pretrained(\n",
    "    \"Salesforce/blip-image-captioning-base\",\n",
    "    ms_dtype=mindspore.float16,\n",
    ")\n",
    "model = BlipForConditionalGeneration.from_pretrained(\n",
    "    \"Salesforce/blip-image-captioning-base\",\n",
    "    ms_dtype=mindspore.float16,\n",
    "    low_cpu_mem_usage=True,\n",
    ")\n",
    "\n",
    "model.set_train(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ce5051-25ef-4458-b803-3dffb0281d76",
   "metadata": {},
   "source": [
    "# 固定输入\n",
    "固定模型输入图像大小，避免每次传入新尺寸图像都需要重新编译花费大量时间 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e66fe484",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预编译模型（首次运行需要1-2分钟）...\n",
      "预编译完成！耗时: 214.89秒\n"
     ]
    }
   ],
   "source": [
    "# 创建固定尺寸的输入模板 - 避免每次重新编译\n",
    "fixed_size = (384, 384)  # 使用BLIP的标准输入尺寸\n",
    "dummy_image = Image.new(\"RGB\", fixed_size, color=(0, 0, 0))\n",
    "dummy_inputs = processor(dummy_image, return_tensors=\"ms\")\n",
    "\n",
    "print(\"预编译模型（首次运行需要1-2分钟）...\")\n",
    "start = time.time()\n",
    "_ = model.generate(**dummy_inputs, max_length=20)\n",
    "print(f\"预编译完成！耗时: {time.time() - start:.2f}秒\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05ecde7-3f57-47ce-a99a-dd4ed7df3f81",
   "metadata": {},
   "source": [
    "# 模型推理\n",
    "定义模型处理图像的方法，并将模型推理结果返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7dc16ff9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_image(image_path):\n",
    "    \"\"\"读取图片并生成描述\"\"\"\n",
    "    if not os.path.exists(image_path):\n",
    "        return \"❌ 文件不存在\"\n",
    "\n",
    "    cv_image = cv2.imread(image_path)\n",
    "    if cv_image is None:\n",
    "        return \"❌ 无法读取图像\"\n",
    "\n",
    "    # 转 RGB + 缩放\n",
    "    cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)\n",
    "    cv_image = cv2.resize(cv_image, fixed_size)\n",
    "    raw_image = Image.fromarray(cv_image)\n",
    "\n",
    "    # 推理\n",
    "    inputs = processor(raw_image, return_tensors=\"ms\")\n",
    "    outputs = model.generate(**inputs, max_length=30, do_sample=False, num_beams=1)\n",
    "\n",
    "    caption = processor.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "    # 显示图片\n",
    "    display(Image.open(image_path))\n",
    "\n",
    "    return caption"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2052c466-d475-4365-b356-81920872e269",
   "metadata": {},
   "source": [
    "# 启动图生文应用\n",
    "创建一个用户交互输入框，用户可以将要推理的模型路径输入至输入框中，模型会将用户输入图像及推理结果展示。  \n",
    "输入stop终止程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e65e54c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入图像路径\n",
      "用户:  /opt/image2text/shixi/test.png\n",
      "\n",
      "Blip："
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.WebPImagePlugin.WebPImageFile image mode=RGB size=359x198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a tree with pink flowers against a blue sky\n",
      "请输入图像路径\n",
      "用户:  stop\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    print(\"请输入图像路径\")\n",
    "    img_path = input(\"\\n用户：请输入图像路径\")\n",
    "    print(\"用户: \",img_path)\n",
    "    if img_path.strip() == \"stop\":\n",
    "        break\n",
    "    print(\"\\nBlip：\", end=\"\")\n",
    "    infer = process_image(img_path)\n",
    "    print(infer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d17c5c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "img",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.23"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
